Reshaping Geotechnical Engineering with Machine Learning

Theory, Applications, and Innovations
Erscheinungsjahr 2026
ISBN: 978-0-443-45276-5
Verlag: Elsevier Science

Buch, Englisch, 325 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 449 g

Reshaping Geotechnical Engineering with Machine Learning: Theory, Applications, and Innovations explores the transformative impact of machine learning (ML) on the field of geotechnical engineering. The book begins by examining the broad applications of ML in key areas such as foundation engineering and slope stability, demonstrating how advanced algorithms can enhance predictive accuracy and decision-making. It emphasizes the importance of robust data acquisition and preprocessing techniques, which are critical for the successful implementation of ML models in geotechnical contexts. The text examines the use of machine learning for predicting soil behavior, a complex challenge in geotechnical engineering, and highlights its role in risk assessment and management. It also addresses the integration of ML with finite element modeling to improve the analysis of tunnel and underground stability. The applications of machine learning in understanding geotechnical materials further showcase the versatility of these techniques. Through detailed case studies, the book illustrates practical implementations of machine learning, bridging theory and real-world problem-solving. It also covers experimental investigations, including laboratory and field studies, which provide essential data for model training and validation. Additionally, the book discusses failure diagnosis of rock slopes by combining discontinuity analysis with numerical modeling, underscoring the potential of ML to enhance safety and reliability in geotechnical projects. This comprehensive resource highlights how machine learning is revolutionizing geotechnical engineering, offering innovative tools and methodologies that improve efficiency, accuracy, and safety in the discipline.

Autoren/Hrsg.

Kumar, Divesh Ranjan

Samui, Pijush

Thangavel, Pradeep

Wipulanusat, Warit

Fachgebiete

• Technische Wissenschaften Bauingenieurwesen Boden- und Felsmechanik, Geotechnik
• Geowissenschaften Geologie Geologie
• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung

Inhaltsverzeichnis

1. Applications of Machine Learning in Geotechnical Engineering, Foundation Engineering, Slope Stability
2. Data Acquisition and Preprocessing in Geotechnical Engineering
3. Machine Learning for Soil Behaviour Prediction
4. Geotechnical Risk Assessment and Management with Machine Learning
5. Tunnel and underground stability using Finite Element Modelling and Machine Learning
6. Geotechnical Material and Machine Learning Applications
7. Case Studies in Machine Learning for Geotechnical Engineering
8. Experimental Investigations: Laboratory and Field Studies
9. Failure Diagnosis of Rock Slopes Using Discontinuity Analysis and Numerical Modeling

Über Autor(innen)

Samui, Pijush
Dr. Samui is an Associate Professor in the Department of Civil Engineering at NIT Patna, India. He received his PhD in Geotechnical Engineering from the Indian Institute of Science Bangalore, India, in 2008. His research interests include geohazard, earthquake engineering, concrete technology, pile foundation and slope stability, and application of AI for solving different problems in civil engineering. Dr. Samui is a repeat Elsevier editor but also a prolific contributor to journal papers, book chapters, and peer-reviewed conference proceedings.

Wipulanusat, Warit
Dr. Warit Wipulanusat is an Associate Professor at the Faculty of Engineering, Thammasat University, Thailand, and a lecturer in the MBA program at Thammasat University. He earned his doctoral degree in Construction Management from the Griffith School of Engineering, Griffith University. As the Head of the Thammasat University Research Unit in Data Science and Digital Transformation, Dr. Warit leads research efforts in applying machine learning and soft computing techniques across various civil engineering disciplines.

Thangavel, Pradeep
Dr. Pradeep Thangavel is a post-doctoral fellow at Thammasat AI Center, College of Innovation, Thammasat University Bangkok. He received an M.E. in structural engineering from Anna University and a Ph.D. from NIT Patna. He was formerly working as an assistant professor at NIT Andhra Pradesh. His primary research interests centre around Building materials like cold-form steel, concrete in steel tubes, and Sustainable Materials in concrete. Furthermore, he is actively researching the use of machine learning in the structural engineering industry and rock. With publications in national and international journals, his research has substantially contributed to the academic community.

Kumar, Divesh Ranjan
Dr. Divesh Ranjan Kumar is a post-doctoral fellow at Research unit in data science and digital transformation, department of civil engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, Thailand. He holds a distinguished academic background, having completed both his M.Tech and Ph.D. in Geotechnical Engineering from the esteemed NIT Patna. In addition to his research, Dr. Kumar's work often integrates advanced machine learning techniques to address complex geotechnical challenges, such as predicting probability of liquefaction potential, finite element modelling, the unconfined compressive strength of controlled low-strength materials using fly ash and pond ash. In addition to his research, Dr. Kumar is actively involved in the scholarly community, with publications in international journals, national journals, participating in international conferences and contributing to scholarly publications. His dedication to advancing civil engineering through innovative research and collaboration underscores his role as a leading figure in his areas of expertise.